marimo-pair

name	marimo-pair
description	Work inside a running marimo notebook's kernel — execute code, create cells, and build a notebook as an artifact. Use when the user wants to start a marimo notebook or work in an active marimo session.
allowed-tools	Bash(bash ${CLAUDE_SKILL_DIR}/scripts/*.sh*), Read

marimo Pair Programming Protocol

This skill gives you full access to a running marimo notebook. You can read cell code, create and edit cells, install packages, run cells, and inspect the reactive graph — all programmatically. The user sees results live in their browser while you work through bundled scripts or MCP.

Philosophy

marimo notebooks are a dataflow graph — cells are the fundamental unit of computation, connected by the variables they define and reference. When a cell runs, marimo automatically re-executes downstream cells. You have full access to the running notebook.

• Cells are your main lever. Use them to break up work and choose how and when to bring the human into the loop. Not every cell needs rich output — sometimes the object itself is enough, sometimes a summary is better. Match the presentation to the intent.
• Understand intent first. When clear, act. When ambiguous, clarify.
• Follow existing signal. Check imports, pyproject.toml, existing cells, and dir(ctx) before reaching for external tools.
• Stay focused. Build first, polish later — cell names, layout, and styling can wait.

Prerequisites

The marimo server must be running with token and skew protection disabled.

How to invoke marimo

marimo must be invoked with --no-token --no-skew-protection to be discoverable. The right way to invoke it depends on context (project tooling, global install, sandbox mode). See finding-marimo.md for the full decision tree.

Do NOT use --headless unless the user asks for it. Omitting it lets marimo auto-open the browser, which is the expected pairing experience. If the user explicitly requests headless, offer to open it with open http://localhost:<port>.

How to Discover Servers and Execute Code

Two operations: discover servers and execute code.

Operation	Script	MCP
Discover servers	bash scripts/discover-servers.sh	list_sessions() tool
Execute code	bash scripts/execute-code.sh -c "code"	execute_code(code=..., session_id=...) tool
Execute code (complex)	bash scripts/execute-code.sh /tmp/code.py	same

Scripts auto-discover sessions from the registry on disk. Use --port to target a specific server when multiple are running. If the server was started with --mcp, you'll have MCP tools available as an alternative.

No servers running?

Always discover before starting. Background task "completed" notifications do not mean the server died — check the output or run discover first.

If no servers are found, read the user's intent — if they want a notebook, start one. Always start marimo as a background task (using run_in_background on the Bash tool) so the server automatically gets cleaned up when the session ends and doesn't block the conversation. See finding-marimo.md.

If there's no .py file yet, pick a descriptive filename based on context (e.g., exploration.py, analysis.py, dashboard.py). Don't ask — just pick something reasonable.

Use a file for complex code. When code contains quotes, backticks, ${} template literals, or multiline strings (common with anywidget ESM modules), write the code to a temp file with the Write tool first, then pass the file path as a positional argument. This avoids shell escaping issues entirely. Write temp files under /tmp/mo-<id>/ where <id> is a short random ID you pick once per session, to avoid collisions with stale files from other sessions.

Inline ESM in cell code. Temp files are for execute-code.sh transport only — never for runtime. Use """ for ESM inside ''' for the cell code.

Executing Code

Every execute-code call runs inside the notebook's kernel. All cell variables are in scope — print(df.head()) just works. Nothing you define persists between calls (variables, imports, side-effects all reset), but you can freely introspect the notebook: inspect variables, test code snippets, check types and shapes. Use this to explore, prototype, and validate before committing anything to the notebook — then create cells to persist state and make results visible to the user.

To mutate the notebook's dataflow graph — create, edit, and delete cells, install packages, and run cells — use marimo._code_mode:

import marimo._code_mode as cm

async with cm.get_context() as ctx:
    cid = ctx.create_cell("x = 1")
    ctx.install_packages("pandas")
    ctx.run_cell(cid)

You must use async with — without it, operations silently do nothing. All ctx.* methods are synchronous — they queue operations and the context manager flushes them on exit. Do not await them.

Cells are not auto-executed. create_cell and edit_cell are structural changes only — use run_cell to queue execution.

code_mode is a tested, safe API for notebook mutations — prefer it for all structural changes. You also have access to marimo internals from the kernel, but treat that as a last resort and only with high confidence after exploration.

UI state lives outside the reactive graph. Anywidget traitlets can be read or set directly (e.g., slider.value = 5). For mo.ui.* elements, use ctx.set_ui_value(element, new_value) inside code_mode.

First Step: Explore the API

The code_mode API can change between marimo versions — and each running server could be a different version. Inspect what's available at the start of each session, especially when switching between servers.

import marimo._code_mode as cm

async with cm.get_context() as ctx:
    ctx  # inspect me — dir(), help(), .cells, ...

Guard Rails

Skip these and the UI breaks:

• Install packages via ctx.install_packages(), not uv add or pip. The code API handles kernel restarts and dependency resolution correctly. Only fall back to external CLIs if the API is unavailable or fails.
• Custom widget = anywidget. For bespoke visual components, use anywidget with HTML/CSS/JS. Composed mo.ui is fine for simple forms and controls. See rich-representations.md.
• NEVER write to the .py file directly while a session is running — the kernel owns it.
• No temp-file deps in cells. pathlib.Path("/tmp/...") in cell code is a bug.
• Avoid empty cells. Prefer edit_cell into existing empty cells rather than creating new ones. Clean up any cells that end up empty after edits.
• Don't worry about cell names. Most cells don't need explicit names — see notebook-improvements.md.

Widgets and Reactivity

Anywidget state (traitlets) lives outside marimo's reactive graph. To hook a widget trait into the graph, pick one strategy per widget — never mix them:

• mo.state + .observe() — you pick specific traits to bridge. Default choice.
• mo.ui.anywidget() — wraps all synced traits into one reactive .value. Convenient but coarser.

Read rich-representations.md before wiring either.

Keep in Mind

• The user is editing too. The notebook can change between your calls — re-inspect notebook state if it's been a while since you last looked.
• Deletions are destructive. Deleting a cell removes its variables from kernel memory — restoring means recreating the cell and re-running it and its dependents. If intent seems ambiguous, ask first.
• Installing packages changes the project. ctx.install_packages() adds real dependencies — confirm when it's not obvious from context.

References

• finding-marimo.md — how to find and invoke the right marimo
• gotchas.md — cached module proxies and other traps
• rich-representations.md — custom widgets and visualizations
• notebook-improvements.md — improving existing notebooks